Mammography apparatus

ABSTRACT

A method of processing a given region of interest (ROI) of an X-ray image of a person&#39;s breast to determine presence of a malignancy, the X-ray image having X-ray pixels that indicate intensity of X-rays that passed through the breast to generate the image, the method comprising: for each given X-ray pixel in the given ROI and each of a selection of J(r) X-ray pixels at respective pixel radii PR(r), 1≤r≤R, from the given x-ray pixel, determining a binary number that provides a measure X-ray intensity indicated by the selected X-ray pixel relative to X-ray intensity indicated by the given X-ray pixel; using the determined binary numbers for the selected X-ray pixels at each pixel radius PR(r) to determine a decimal number for the pixel radius PR(r); histogramming the frequency of occurrence of values of the determined decimal numbers as a function of pixel radius for the given X-ray pixels in the given ROI;determining a texture feature vector, for the given ROI having components that are equal to the frequencies of occurrence for a selection of M histogrammed values; and processing the histogrammed frequencies of occurrence for the M values to determine whether the given ROI is malignant.

RELATED APPLICATIONS

The present application is a continuation of U.S. application Ser. No.17/163,934 filed on Feb. 1, 2021, which is a continuation of U.S.application Ser. No. 16/699,660 filed on Dec. 1, 2019, now U.S. Pat. No.10,905,392, which is a continuation of U.S. application Ser. No.15/750,514 filed on Feb. 6, 2018, now U.S. Pat. No. 10,499,866, filed asa National Phase of PCT Application No. PCT/IB2016/054707 filed on Aug.4, 2016, which claims the benefit under 35 U.S.C. 119(e) of U.S.Provisional Application 62/201,774 filed on Aug. 6, 2015 and U.S.Provisional Application 62/260,549 filed on Nov. 29, 2015 thedisclosures of which are incorporated herein by reference.

TECHNICAL FIELD

Embodiments of the disclosure relate to apparatus and methods fordiagnosing breast cancer.

BACKGROUND

X-ray imaging of the human breast to detect malignancies has beenclinically available since about 1970. As might very well be expected,since then, over the period of almost half a century the technology hasundergone a number of changes and refinements. Initially, X-ray imageswere captured on film. Today X-ray breast images are directly capturedby arrays of small X-ray detectors which convert intensity of X-raysincident on the detectors to electrical signals. The electrical signalsare digitized to provide digital representations of the images that arestored in computers for later diagnoses.

A relatively recent change in X-ray breast imaging technology that wasmade practical by digital X-ray imaging is referred to as spectralcontrast enhanced digital mammography (SCEDM). In SCEDM a patient isinjected with a contrast agent that is preferably taken up by cancerouslesions in the patient's breast. The breast is exposed to X-rays at twodifferent energies, typically a relatively low X-ray energy at which thecontrast agent is a relatively poor absorber of X-rays and a relativelyhigh X-ray energy at which the contrast agent is a relatively goodabsorber of X-rays. The exposures to the high and low energy X-raysprovide high and low energy X-ray digital images respectively of thebreast. The images are digitally subtracted to provide a “subtractedimage” in which concentrations of the contrast agent in the breast, andthereby malignant lesions in the breast, generally have enhancedcontrast and visibility. In particular, for dense breast tissues, knownto be relatively opaque in classical mammography, normal breast tissuebecomes substantially transparent in the subtracted image, enhancingcontrast of lesions that in conventional non-subtracted X-ray images maybe difficult to discern. Typically, the contrast agent used to acquirethe high and low X-ray images is an iodine based contrast agent, and thelow energy X-rays have an energy below the k-edge of iodine and the highenergy X-rays have an energy above the k-edge of iodine.

Although SCEDM has the potential to improve sensitivity of mammography,in practice, a relatively large number of biopsies is performed onlesions detected in SCEDM images that turn out to be benign. More than60% of the biopsies triggered by a lesion detected in SCEDM are actuallyperformed on benign lesions. The relatively large number of biopsiesthat turn out to be unnecessary imposes a relatively high financial costand psychological burden on patients and society.

SUMMARY

An aspect of an embodiment of the disclosure relates to providingapparatus for diagnosing presence of breast malignancies in a patient'sbreast, the apparatus comprising an X-ray imager configured to acquirean SCEDM image of the breast and a processor configured to process theimage to provide a determination of the presence of malignancies. In anembodiment, the processor processes the SCEDM image to generate an imagefeature vector for a contrast enhanced region of interest (CE-ROI) inthe SCEDM that is a function of morphology and/or texture of the CE-ROI.The processor uses the CE-ROI feature vector, to provide a determinationas to whether or not the CE-ROI comprises a malignancy. A determinationmay comprise an estimate of a probability that a CE-ROI comprises amalignancy. The processor may also use a context feature vector,hereinafter also referred to as a “profile feature vector”, based on apersonal profile of the patient, to provide the determination as towhether or not the CE-ROI comprises a malignancy. In an embodiment, theprocessor operates on the CE-ROI feature vector and/or the profilefeature vector in accordance with a classifier to determine whether ornot the CE-ROI comprises a malignancy. Optionally, the classifiercomprises a support vector machine (SVM) or a neural network.

Experiments carried out on SCEDM images acquired from actual patientsand for which biopsies were performed indicate that apparatus, inaccordance with an embodiment of the disclosure, hereinafter alsoreferred to as an X-ray Breast Imager (XBI) may provide sensitivity todetecting breast malignancies that ranges from about 90% to 100% withassociated specificities that range respectively from about 70% to about37%. For an embodiment for which sensitivity is equal to about 98.1%associated specificity was equal to about 53.7%. It is noted that humaninspection of SCEDM images to detect breast malignancies typicallyprovide sensitivity of about 90%-95% and associated specificity of about55%-65%.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

BRIEF DESCRIPTION OF FIGURES

Non-limiting examples of embodiments of the disclosure are describedbelow with reference to figures attached hereto that are listedfollowing this paragraph. Identical features that appear in more thanone figure are generally labeled with a same label in all the figures inwhich they appear. A label labeling an icon representing a given featureof an embodiment of the disclosure in a figure may be used to referencethe given feature. Dimensions of features shown in the figures arechosen for convenience and clarity of presentation and are notnecessarily shown to scale.

FIG. 1A schematically shows an X-Ray breast imager (XBI) for acquiringSCEDM images of a breast, in accordance with an embodiment of thedisclosure;

FIG. 1B shows a flow diagram in accordance with which the XBIschematically shown in FIG. 1A acquires SCEDM images, in accordance withan embodiment of the disclosure;

FIG. 2A shows a flow diagram of a procedure by which a XBI processes anSCEDM image of a breast to generate and use a feature vector todetermine whether the image indicates presence of malignancy in thebreast, in accordance with an embodiment of the disclosure;

FIG. 2B shows a flow diagram of a procedure by which a XBI may configurea texture feature vector for use with the procedure illustrated in FIG.2A in diagnosing malignancy, in accordance with an embodiment of thedisclosure;

FIG. 3 schematically shows an SCEDM image of a breast comprising acontrast enhanced region of interest (CE-ROI) having a contour processedin accordance with the procedure flow diagram show in in FIG. 2 toprovide a contour feature vector “CF” for diagnosing malignancy, inaccordance with an embodiment of the disclosure;

FIG. 4 schematically shows pixels that image the CE-ROI in the X-raybreast image shown in FIG. 3 and are processed in accordance with theprocedure flow diagram show in in FIG. 2 to provide a texture featurevector “TF” for diagnosing malignancy, in accordance with an embodimentof the disclosure; and

FIG. 5 shows a distribution of classification scores generatedresponsive to texture feature vector TFs determined for CE-ROIs in SCEDMX-ray images acquired for patients for which biopsies were preformed,and different threshold values for determining whether the scoresindicate malignancy, in accordance with an embodiment of the disclosure.

DETAILED DESCRIPTION

In the discussion, unless otherwise stated, adjectives such as“substantially” and “about” modifying a condition or relationshipcharacteristic of a feature or features of an embodiment of thedisclosure, are understood to mean that the condition or characteristicis defined to within tolerances that are acceptable for operation of theembodiment for an application for which the embodiment is intended.Wherever a general term in the disclosure is illustrated by reference toan example instance or a list of example instances, the instance orinstances referred to, are by way of non-limiting example instances ofthe general term, and the general term is not intended to be limited tothe specific example instance or instances referred to. Unless otherwiseindicated, the word “or” in the description and claims is considered tobe the inclusive “or” rather than the exclusive or, and indicates atleast one of, or any combination of more than one of items it conjoins.

FIG. 1A schematically shows an X-Ray breast imager (XBI) 20 foracquiring SCEDM images of a breast, in accordance with an embodiment ofthe disclosure. XBI 20 optionally comprises a controller 50, an X-raysource 30 configured to generate a beam of X-rays for acquiring SCEDMimages of a breast, and a breast holder 22 for holding and positioning abreast in the X-ray beam provided by the X-ray source. X-rays generatedby X-ray source 30 are schematically indicated by dashed lines 45.Embodiments of controller 50 may comprise any electronic and/or optical,memory, processing, and/or control circuitry advantageous for providingcontroller functionalities. By way of example, controller 50 may,comprise any one or any combination of more than one of, amicroprocessor, an application specific circuit (ASIC), fieldprogrammable array (FPGA) and/or system on a chip (SOC). The controllermay comprise any one or any combination of more than one of a flashmemory, random access memory (RAM), read only memory (ROM), and/orerasable programmable read-only memory (EPROM). And whereas controller50 is shown in FIG. 1A as a single localized entity, the controller maybe configured as a distributed entity or at least in part a cloud basedentity.

Breast holder 22 comprises a breast support plate 23 and a breastcompressor plate 26 for compressing a breast positioned on support plate23 in preparation for exposure to X-rays 45 to acquire an X-ray image ofthe breast. Breast support plate 23, comprises a digital X-ray camera 24having an array of X-ray sensors 25, each sensor 25 operable to generateelectronic signals responsive to intensity of X-rays 45 that passthrough the breast held in breast holder 22. Compressor plate 26 is madefrom a material such as a suitable plastic that is substantiallytransparent to X-rays 45. X-ray source 30 and breast holder 22 areoptionally mounted to a support beam 40 attached to a column support 41by a shaft 43. Column support 41 is attached to a base 42 for floormounting XBI 20.

Support beam 40 and shaft 43 may be configured to slide up and downalong column support 41 to adjust height of breast holder 22 to apatient's height, and to enable the support beam to rotate about an axisof rotation, indicated by a dashed line 44, of shaft 43 so that X-Rayimages of the breast, may be acquired from various angles. By way ofexample, support beam 40 is schematically shown in FIG. 1A orientedvertically parallel to column support 41 so that XBI 20 may acquire aCranial-Caudal (CC) X-ray image of a breast. Rotation of support beam 40away from the vertical enables XBI 20 to acquire a mediolateral-oblique(MLO) image views of the breast.

FIG. 1B shows a flow diagram 100 illustrating a procedure by which XBI20 may operate to acquire SCEDM images of a breast (not shown).Initially, in block 101, a contrast agent, which is preferentially takenup in regions of increased vascularity, such as in growing tumors, isinjected into a patient (not shown) in preparation for imaging a breast(not shown) of the patient. Optionally, in a block 103 the patient'sbreast is positioned between breast support plate 23 and breastcompressor plate 26 and compressed, to even out the thickness of thebreast tissue and avoid folding of the breast tissue which can obscureinternal features of the breast, in preparation for acquiring,optionally, MLO X-ray images of the breast. For acquiring the MLO X-rayimage support beam 40 shown in FIG. 1A is substantially perpendicular tocolumn support 41 and parallel to the floor. In a block 105 controller50 controls X-ray source 30 and digital X-ray camera 24 to expose thebreast to low energy X-rays and acquire a low energy MLO X-ray image ofthe breast. In a block 107 the controller controls X-ray source 30 andX-ray camera 24 to expose the breast to high energy X-rays and acquire ahigh energy MLO X-ray image of the breast. The breast may then bereleased in a block 109 and controller 50 (FIG. 1A) may in a block 111process the high and low energy X-ray images to provide a MLO SCEDMimage by subtracting the low energy X-ray image from the high energyX-ray image. In a block 113 controller 50 may rotate support beam 40 ofXBI 20 (FIG. 1A) so that it is parallel to support beam 41, as shown inFIG. 1A, to orient X-ray source 30 and breast holder 22 for X-rayimaging of the breast at a CC view. In a block 115 the breast isoptionally again positioned between the breast support plate 23 andbreast compressor plate 26, and the latter is moved towards breastsupport plate 23 to compress the breast and even out breast tissue. Aseries of two X-ray exposures are made of the breast to acquire a “lowenergy CC X-ray image”, optionally in a block 117 and a “high energy CCX-ray image” optionally in a block 119. The breast may then be releasedin a block 121 and in a block 123 controller 50 processes the low andhigh CC X-ray images to generate a CC SCEDM image.

It is noted that contrast agents that preferentially accumulate inmalignant lesions and are used in SCEDM breast imaging are generallyiodinated agents. Therefore to acquire low energy images in SCEDMimaging of breast tissue the energy of the X-rays provided by X-raysource 30 in XBI 20 is below the k-edge of iodine. To acquire the highenergy images the energy of X-rays provided by X-ray source 30 is abovethe k-edge of the iodine in the contrast agent.

In an embodiment of the disclosure, controller 50 (FIG. 1A) may processan SCEDM X-ray image of a patient's breast acquired by XBI 20 todetermine presence of a malignancy in accordance with a procedureillustrated by a flow diagram 140 shown in FIG. 2 . The numerical label140 may be used to refer to the procedure illustrated by flow diagram140 as well as to the flow diagram. FIG. 3 shows an example SCEDM X-rayimage 200 acquired for a breast of a patient. SCEDM X-ray image 200exhibits a CE-ROI 202, shown greatly enlarged in FIG. 3 in an inset 203,that may comprise a malignancy and may be processed by controller 50 inaccordance with procedure 140. FIG. 4 schematically shows pixels in aregion of CE-ROI 202 greatly enlarged in an inset 250 to illustrate howthey may be processed to provide a texture feature vector TF for use indiagnosing breast malignancy, in accordance with procedure 140. Featuresshown in FIGS. 3 and 4 may be referred to where relevant in the flowdiagram of FIG. 2 by their labels in FIGS. 3 and 4 .

In a block 141 of procedure 140 controller 50 may receive via a suitableuser interface (not shown) to the controller, a segmentation of X-rayimage 200 shown in FIG. 3 made by a medical practitioner that locatesCE-ROIs, of which CE-ROI 202 is an example, in the image. Alternativelyor additionally controller 50 may process the X-ray image using asuitable segmentation algorithm to locate CE-ROIs. In a block 143 acontour 204 of CE-ROI 202 may be determined. The contour may bedetermined automatically by controller 50 or may be manually determinedand input to controller 50 by a user operating the interface to thecontroller.

Optionally, in a block 145, controller 50 processes contour 204 toprovide components of a contour feature vector CF(N) characterizing thecontour and having components CF(n), 1≤n≤N. Components CF(n) maycomprise, by way of example, a length of contour 204, a number ofturning points in the contour, entries in a histogram of heights ofpositive turning points, and entries in a histogram of widths of peaksin the contour.

Turning points in contour 204 are points along the contour at which aderivative as a function of a parameter measuring displacement along thecontour is zero. A positive turning point is a point at which contour204 is concave facing inwards to the area of CE-ROI 202 surrounded bythe contour. A negative turning point is a point at which contour 204 isconvex facing inwards to the area of CE-ROI 202. Example turning pointsin contour 204 are indicated by a triangle pointer 206 and may bereferred to by the numeral 206. A height of a positive turning point 206may be a distance between the positive turning point and an adjacentnegative turning point along contour 204. An example height of apositive turning point is indicated in FIG. 3 by a distance 208. Inaccordance with an embodiment, entries in a histogram of heights ofpositive turning points 206 in contour 204 may be features of featurevector CF(N). A peak of contour 204 may be a segment of contour 204between two negative turning points adjacent to and located on oppositesides of a positive turning point in the contour. Width of the may be adistance between points on the contour on opposite sides of the peak'spositive turning point located at respective half heights to the peak'snegative turning points. A peak width of contour 204 is indicated by anumeral 210. Entries in a histogram of peak widths in contour 204 may befeatures of feature vector CF(N) in accordance with an embodiment of thedisclosure.

Optionally, in a block 147, for each X-ray pixel “P(x,y)” of X-ray image200 located in CE-ROI 202 at row and column image coordinates (x,y) ofthe X-ray image, controller 50 determines a neighborhood, optionallyreferred to as a “texture neighborhood”, of X-ray pixels in CE-ROI 202for processing to determine a measure of texture of CE-ROI 202. Theneighborhood, also referred to as a “texture neighborhood” extends“radially” a “radial pixel distance”, also referred to as a “pixelradius” (PR), equal to a bounding pixel radius, “BPR”, pixels in anydirection from location (x,y) of X-ray pixel P(x,y).

FIG. 4 schematically shows a texture neighborhood 230 determined for anX-ray pixel P(x,y) in CE-ROI 202. Texture neighborhood 230 may extend toa radial pixel distance PR=BPR greater than or equal to about 25 pixels.Optionally, texture neighborhood 230 extends to a bounding pixel radiusBPR of about 50 pixels or more. In an embodiment, texture neighborhood230 extends to a bounding pixel radius BPR of at least about 75 pixels.A portion of texture neighborhood 230 that extends to a pixel radius PRof 7 pixels is schematically shown greatly enlarged in an inset 250 inthe figure. X-ray pixels 231 are adjacent to pixel P(x,y) and arelocated at a pixel radius PR equal to 1 pixel from X-ray pixel P(x,y),while X-ray pixels 232 are located at a pixel radius PR=4 pixels fromX-ray pixel P(x,y).

In a block 149, controller 50 selects X-ray pixels at R different pixelradii PR(r), 1≤r≤R, in texture neighborhood 230 for processing todetermine a measure of texture for CE-ROI 202. Pixel radii PR(r)increase with increase in index r, and PR(R) is equal to the boundingpixel radius BPR of texture neighborhood 230. R may be equal to at least2. Optionally, R is equal to or greater than about 10. Optionally R isgreater than 20. By way of a numerical example, in the discussion thatfollows BPR for texture neighborhood 230 is assumed to be 64 pixels andR equal to 4. For convenience of presentation a pixel radius PR(r) maybe referred to by its index as pixel radius r.

For each pixel radius r, 1≤r≤R, controller 50 selects, optionally a samenumber “J”, of X-ray pixels S(x,y,r,j), 1≤j≤J for processing.Optionally, J is equal to or greater than 4. In an embodiment J is equalto or greater than 8. By way of example, in FIG. 4 , J=8, thereforeeight X-ray pixels S(x,y,r,j) are selected from the X-ray pixels foreach pixel radius, r=1, 2, 3, and 4. Selected pixels S(x,y,r,j) areindicated in FIG. 4 by shading and for convenience of presentation areshown for only pixel radii PR(1) and PR(2) as examples equalrespectively to 2 pixels and 4 pixels. It is noted that practice of anembodiment of the disclosure is not limited to pixel radii PR(1)=2 andPR(2)=4. In an embodiment, by way of numerical example, for textureneighborhood 230 having BPR=64 and R=4 as noted above, pixel radiiPR(1), PR(2), PR(3), and PR(4) in the texture neighborhood may be equalto 15, 30, 45 and 70 pixels respectively. Optionally, the selectedpixels S(x,y,r,j) are symmetrically located around pixel P(x,y), alongstraight lines extending from pixel P(x,y). Optionally, selected pixelsS(x,y,r,j) are located along lines extending from pixel P(x,y) every45°, at angles (j−1)45° respectively. The lines are labeled with thevalue of the index j of the X-ray pixels S(x,y,r,j) associated with thelines. Selected X-ray pixels having index j=1, that is pixelsS(x,y,r,1), lie along a 12 o'clock direction, and angles increase withincrease in j in a clockwise direction.

SIn a block 151 controller 50 assigns a binary number 0 or 1, to eachselected X-ray pixel S(x,y,r,j), depending on whether intensity ofincident X-rays represented by a gray level of the selected X-ray pixelis respectively less than or greater than incident X-ray intensityrepresented by a gray level of pixel P(x,y). Let P(x,y) and S(x,y,r,j),in addition to identifying particular X-ray pixels in CE-ROI 202,represent intensity of X-rays that gray levels of the identified pixelsrespectively represent. Then the calculation performed in block 151 maybe represented in symbols as: IF (S(x,y,r,j)<P(x,y), b(x,y,r,j)=0;otherwise, b(x,y,r,j)=1).

By way of example, arbitrary gray levels are shown in FIG. 4 for X-raypixel P(x,y) and selected X-ray pixels S(x,y,1,j) that are located atpixel radius r=1 (that is PR(1)=2 pixels) from P(x,y). The arbitrarygray level for pixel P(x,y) is 79, and gray levels for selected X-raypixels S(x,y,1,1)-S(x,y,1,8) are 99, 20, 34, 78, 52, 88, 70, 34respectively. The selected X-ray pixels (S(x,y,1,j) are thereforeassigned in block 151 corresponding binary numbers b(x,y,1,1), . . . ,b(x,y,1,8) that are respectively equal to 1, 0, 0, 0, 0, 1, 0, 0.

Optionally, in a block 153 controller 50 assigns a binary numberBN(x,y,r) comprising J bits for each pixel radius r in CE-ROI 202. Aj-th bit of binary number BN(x,y,r) is equal to b(x,y,r,j) and a mostsignificant bit in the binary number is optionally b(x,y,r,1). Forexample, for CE-ROI 202, BN(x,y,1) is determined from the binary numbersb(x,y,1,1)-b(x,y,1,8) discussed above, and is equal to 10000100. In asubsequent block 155, the controller may convert each binary numberBN(x,y,r) to a decimal number N(x,y,r) that may assume any of2{circumflex over ( )}J values between 0 and (2{circumflex over( )}J−1). And in a block 157, the controller assigns an R dimensionaltexture vector TV(x,y) for X-ray pixel P(x,y) having R components:N(x,y,1), N(x,y,2), . . . , N(x,y,R). For CE-ROI 202 having R=4, and J=8selected X-ray pixels S(x,y,r,j) for each pixel radius r, the decimalnumber N(x,y,r) may assume any of 256 values between and inclusive of 0and 255, and TV(x,y) for X-ray pixel P(x,y) has 4 components.

In a block 159, controller 50 may generate a histogram HTV(T) of thetexture vectors TV(x,y) determined for optionally all X-ray pixelsP(x,y) having locations (x,y) in the area of CE-ROI 202. HTV(T) hasT=R2{circumflex over ( )}J bins, and may be written as the setHTV(T)={HTV(t)|≤t≤T=R2{circumflex over ( )}J}, where HTV(t) is the valueof a t-th bin of the histogram. A value HTV(t) is equal to a number ofX-ray pixels P(x,y) in CE-ROI 202 having a decimal value N(x,y,(t modulo2{circumflex over ( )}J). By way of example, for R=4 and J=8 the numberof bins T is equal to 1024.

In an embodiment of the disclosure, optionally in a block 161,controller 50 selects “M” bins from the T bins of HTV(T) as componentsof a texture feature vector TF(M) for use in diagnosing malignancy inthe breast imaged in X-ray image 200 (FIG. 3 ). In a block 163 thecontroller optionally generates an SCEDM feature vector, “SCEDM-F(·)”for CE-ROI 202 responsive to contour feature vector CF(N) and/or texturefeature vector TF(M) to diagnose malignancy in the CE-ROI. Optionally,SCEDM-F(·) is a K dimensional feature vector SCEDM-F(K), which may bereferred to as a feature vector CF-TF, having dimension K=(N+M)generated by concatenating CF(N) and texture feature vector TF(M).

In a block 165, controller 50 may concatenate SCEDM-F(K) with a Qdimensional profile feature vector PF(Q) of the patient for which X-rayimage 200 was acquired to generate a G=(K+Q) dimensional global featurevector “GF(G)” for diagnosing malignancy in CE-ROI 202. Profile featurevector PF(Q) may comprise any one or any combination of more than one ofpersonal data components, such as age, sex, family data, and geneticdata. A global feature vector formed by concatenating PF and CF-TF maybe referred to as a feature vector PF-CF-TF

In a block 167, controller 50 processes feature vector SCEDM-F(K) orfeature vector GF(G) to determine whether CE-ROI 202 comprises amalignancy. In an embodiment, processing the feature vector comprisesoperating on the feature vector using a classifier such as a supportvector machine (SVM) or a neural network (NN) that has been trained on aset of training images CE-ROIs.

Controller 50 may determine a number M, and which of the components ofthe T components of histogram HTV(T) determined for CE-ROI 202 in block161 of flow diagram 140, are to be selected for components of texturefeature vector TF(M) by processing a plurality of sample CE-ROIs forwhich it is known whether they are benign or malignant. FIG. 2B shows aflow diagram of a procedure 180 illustrating a procedure by whichcontroller 50 may determine how many, and which components of HTV(T) toselect for texture feature vector TF(M), in accordance with anembodiment of the disclosure. Procedure 180 also provides an SVR machinefor classifying CE-ROIs responsive to a texture feature vector TF(M)based on the number and components of HTV(T) selected by the procedurefor features of TF(M).

Assume that the sample CE-ROIs are represented by CE-ROI_(V) 1≤v≤V, andthat for each CE-ROI_(V) controller 50 has acquired a histogramHTV_(V)(T), optionally in accordance with block 159 of procedure 140shown in FIG. 2A. Assume further that of the V samples CE-ROI_(V), Vmare known to be malignant and Vb are known to be benign and thatV=Vm+Vb.

In a block 181 of flow diagram 180, controller 50 calculates an averagevalue μ_(m)(t)=(1/Vm) Σ₁ ^(Vm) HTV_(V)(t) and a variance σ² _(m)(t) ofthe average, for each t-th bin of the Vm histograms determined for theCE-ROI_(V) that are known to be malignant. In a block 183 controller 50calculates an average value μ_(b)(t)=(1/Vb) Σ₁ ^(Vb) HTV_(V)(t) and avariance σ_(b)(t) of the average, for each t-th bin of the Vb histogramsdetermined for the CE-ROI_(V) that are known to be benign. In a block185, the controller may calculate for each t-th bin of the HTV_(V)(T) asignal to noise ratio, “SNR(t)”, which may be defined by an expressionSNR(t)=|μ_(m)(t)−μ_(b) (t)|/sqrt[σ² _(m)(t)+σ² _(b)(t)], and if SNR(t)is greater than a threshold noise, “th-SNR”, the controller selects thet-th bin as a candidate for providing a component of TF(M). Let an SNRselected bin number t be primed and represented by t′ to indicate thatit was selected. For example if a bin number 783 was selected it isrepresented by 783′.

Optionally, in a block 187, controller 50 clusters bins having SNRselected bin numbers t′ into clusters of correlated bins responsive to acorrelation threshold “th-correlation”. Bins that are considered to becorrelated are clustered in a same i-th correlation cluster. For eachi-th correlation cluster a representative bin number t* is selected fromthe bin numbers t′ in the correlation cluster. This correlation processdetermines, a selection of M* bins having bin numbers t*_(m), 1≤t*≤M*,selected to provide values HTV_(V)(t*) for texture feature vectorsTF(M*)_(V) for CE-ROI_(V) respectively. M* is a function of th-SNR andth-correlation, and may be written M*(th-SNR, th-correlation) toindicate the dependence. In a block 191 controller 50 optionallygenerates a texture feature vector TF(M*(th-SNR, th-correlation))comprising a set {HTV(t*_(m)): 1≤m≤M*} as components, where t*_(m)represents a particular representative bin number of the M* differentrepresentative bin numbers.

In a block 191, controller 50 may perform a K-fold cross validationerror evaluation for an SVR model, SVR(TF(M*)_(V)) for the samplesCE-ROI_(V) and their known presence or absence of malignancy using thetexture feature vectors TF(M*)_(V) determined for each of a group of adifferent pairs of values for th-SNR and th-correlation. A pair ofvalues th-SNR and th-correlation from the group that provides a minimumcross validation error in diagnosing malignancy in the CE-ROI_(V) may bedetermined as an optimal pair of values, th-SNR_(O) andth-correlation_(O). M* and bin numbers t_(m)* associated with th-SNR_(O)and th-correlation_(O) are used as the number M and bin numbers t_(m) todefine texture feature vector in TF(M) in block 161 of flow diagram 140.

In an embodiment, in a block 193, classification values λ_(V) forCE-ROI_(V), 1≤v≤V respectively generated by the K-fold cross-validationperformed for th-SNR_(O) and th-correlation_(O), may be used todetermine a corresponding optimal SVM_(O) based on all V members of theplurality of samples CE-ROI_(V). In an embodiment of the disclosureSVM_(O) may be used to diagnose a CE-ROI of a breast X-ray image thatXBI 20 acquires for a patient to classify and determine if the CE-ROI ismalignant.

FIGS. 5A-5C show dispersion graphs of results of using a texture featurevector TF(M) and a SVM_(O) determined in accordance with proceduressimilar to procedures 140 and 180 shown in the flow diagrams of FIGS. 2Aand 2B to classify 93 CE-ROIs segmented by hand from X-ray imagesacquired for actual patients. The dispersion graphs were generated fortexture feature vector, TF(M), parameters: BPR=64 pixels; R=4, PR(1), .. . , PR(4) equal to 15, 30, 45, and 70 pixels, and J=8; M=M*=3 andassociated bin numbers t*_(m)=30, 784, and 896 from a histogram HTV(T).Of the 93 CE-ROIs, 41 were known to be images of benign tissue and 52were known to be images of malignant tissue.

Each of FIGS. 5A-5C shows a same dispersion 500. In the dispersiongraphs, CE-ROIs known to be cancerous are represented by asterisks 501.CE-ROIs known to be benign are represented by circles 502. The 52cancerous CE-ROIs are identified by ID indices 1-52 and the benignCE-ROIs are identified by ID indices 1-41. The abscissas in thedispersion graphs show the ID indices of the CE-ROIs associated with theasterisks and circles. The ordinates of the dispersion graphs show theclassification scores determined for the CE-ROIs by the SVM_(O).Cancerous CE-ROIs have higher classification scores than benign CE-ROIs.Dispersion graphs 5A-5C have classification decision lines A-Crespectively that may be used to make a decision as to whether a givenclassification score is to be considered to indicate that an entry inthe dispersion graphs is cancerous or not. Classification scores above agiven partition line A, B, or C may be considered to indicate thattissue in a CE-ROI represented by an entry in the graph, that is anasterisk or a circle, is malignant, while classification scores belowthe partition line may be considered to indicate that tissue in theCE-ROI is benign. Classification lines A, B, and C are located atclassification scores 0.5, 0, and −0.5.

Graph 5C shows that if the decision line, decision line C in the graph,is set so low as to provide 100% sensitivity, that is, to correctlyclassify all cancerous CE-ROIs as cancerous, XBI 20 will exhibitspecificity of 36.6% and correctly identify 36.6% of the non-cancerousCE-ROIs as benign. Graph 5C indicates that XBI 20 operating inaccordance with an embodiment of the disclosure to implement proceduressimilar to that illustrated by flow diagram 140 and 180, may beconfigured to provide 100% detection of cancerous lesions andpotentially reduce unwanted biopsies by about 36%. Graph 5B, withdecision line B at 0, XBI 20 configured in accordance with an embodimentof the disclosure to provide 98% sensitivity will provide specificity ofabout 53% and potentially reduce unwanted biopsies by about half. Graph5A indicates that XBI 20 configured in accordance with an embodiment ofthe disclosure to reduce unwanted biopsies by about 75% will stillcorrectly detect about 90% of cancerous lesions.

Whereas the above description references specific examples of featuresthat may be used to provide components of a feature vector used toclassify and distinguish malignant from benign lesions based on contrastenhanced images of the lesions, practice of an embodiment of thedisclosure is not limited to the referenced features. By way of example,a feature or any combination of more than one feature listed and definedby the Breast Imaging Reporting and Data System (BIRADS) LexiconClassification System established by the American College of Radiologymay be used to provide a component of a feature vector for classifying alesion in accordance with an embodiment of the disclosure. The BI-RADSclassification system classifies lesions as mass like, three-dimensionalspace occupying lesion, or a non-mass like lesion and for each type oflesion a characteristic of the lesion that may be used to provide adetermination as to whether the lesion should be classified as malignantor benign. Among the characteristics of a mass like lesion listed anddefined in the BIRADS lexicon are by way of example, characteristics ofthe lesion's shape—whether it is round, oval or irregular, andcharacteristics of its margin—whether the margin appears smooth,irregular or spicualted. Among characteristics of a non-mass-lesionlisted and defined in the BIRAD lexicon are by way of example,characteristics of its distribution—whether it is characterized by afocal area, whether is linear, ductal, segmental or diffuse.Furthermore, whereas a classifier may provide classification scores suchas those shown in FIGS. 5A-5C to determine whether a CE-ROI comprises acancer, in an embodiment, the classifier may operate on a feature vectorto provide BIRADS scores for use in determining whether a breast lesionis cancerous or not.

In an embodiment, a feature of a feature vector may be a time dependentfeature. For example, as described with reference to FIGS. 1A-1B, XBI 20may be operated to acquire a plurality of SCEDM images. The images, byway of example, low and high energy MLO X-ray images and low and highenergy CC X-ray images may be acquired at different times. During timelapses between acquisitions of the images concentration of a contrastagent introduced into a patient's body may change as a result of a rateat which the contrast agent is taken up or washed out by breast tissuethat is being imaged. A rate of increase or decrease of the contrastagent in the imaged tissue may be estimated from a change in contrast ofthe imaged tissue exhibited in the images and used to provide acomponent of a feature vector for discriminating malignant from benigntissue in accordance with an embodiment.

For example, a time lapse between acquiring CC and MLO X-ray images of abreast tissue lesion may be as long as 1-2 minutes. If concentration ofa contrast agent in the lesion is substantially maximum duringacquisition of the CC X-ray images, the concentration may besubstantially reduced by wash out at a time at which the MLO X-rayimages are acquired. A difference in contrast between the CC and MLOX-ray images, and the time lapse between acquisition of the images maybe used to provide an estimate of a rate at which blood flow washes outthe contrast agent from the lesion and thereby a characteristic ofmagnitude of blood flow in and through the lesion. The rate of washoutmay be advantageous as a component of a feature vector in accordancewith an embodiment for distinguishing whether the lesion is malignant orbenign.

The above description indicates, by way of example, that identificationof a feature of an SCEDM image of a breast tissue lesion for generatingor using as a component of a feature vector may be determined manually.In an embodiment a convolutional neural network (CNN) may be used toautomatically identify and determine which features of an SCEDM image ofa breast tissue lesion are advantageous for providing a component of afeature vector for classifying lesions as malignant or benign. Uponidentifying and determining which features of an SCEDM image areadvantageous for use as a component of the feature vector, the CNN mayprovide the feature to a classifier that processes the feature vector todetermine malignancy or benignity. Optionally the CNN operates as theclassifier, and processes the feature vector to determine malignancy orbenignity. The CNN may undergo training to learn directly from inputimages to identify and determine which features of an SCEDM to provideto a classifier for classifying lesions as malignant or benign.

By way of example, a CNN may be used to determine a magnitude of awashout rate for a CE-ROI by processing SCEDM images comprising theCE-ROI acquired at different times after the CNN was trained on imagesof malignant breast tissue that exhibit a known rate of washout of acontrast agent. Images used to train the CNN may be SCEDM images oftissue for which information with respect to washout is known from MRIimages of the tissue.

In the description and claims of the present application, each of theverbs, “comprise” “include” and “have”, and conjugates thereof, are usedto indicate that the object or objects of the verb are not necessarily acomplete listing of components, elements or parts of the subject orsubjects of the verb.

Descriptions of embodiments of the disclosure in the present applicationare provided by way of example and are not intended to limit the scopeof the disclosure. The described embodiments comprise differentfeatures, not all of which are required in all embodiments of thedisclosure. Some embodiments utilize only some of the features orpossible combinations of the features. Variations of embodiments of thedisclosure that are described, and embodiments of the disclosurecomprising different combinations of features noted in the describedembodiments, will occur to persons of the art. The scope of thedisclosure is limited only by the claims.

The invention claimed is:
 1. A method of processing a given region ofinterest (ROI) of an X-ray image of a person's breast to determinepresence of a malignancy, the X-ray image having X-ray pixels thatindicate intensity of X-rays that passed through the breast to generatethe image, the method comprising: for each given X-ray pixel in thegiven ROI and each of a selection of J(r) X-ray pixels at respectivepixel radii PR(r), 1≤r≤R, from the given x-ray pixel, determining abinary number that provides a measure X-ray intensity indicated by theselected X-ray pixel relative to X-ray intensity indicated by the givenX-ray pixel; using the determined binary numbers for the J(r) selectedX-ray pixels at each pixel radius PR(r) to determine a decimal numberfor the pixel radius PR(r); histogramming the frequency of occurrence ofvalues of the determined decimal numbers as a function of pixel radiusfor the given X-ray pixels in the given ROI; determining a texturefeature vector, for the given ROI having components that are equal tothe frequencies of occurrence for a selection of M histogrammed values;and processing the histogrammed frequencies of occurrence for the Mvalues to determine whether the given ROI is malignant.
 2. The methodaccording to claim 1 and comprising determining an average μ_(m) andvariance σ² _(m) for the frequency of occurrence for each histogrammeddecimal value determined for a plurality of ROIs that are known to bemalignant and using the μ_(m) and σ² _(m) to determine the selected Mhistogrammed values.
 3. The method according to claim 2 and comprisingdetermining an average μ_(b) and variance σ² _(b) for the frequency ofoccurrence for each histogrammed decimal value determined for aplurality of ROIs that are known to be benign and using the μ_(b) and σ²_(b) to determine the selected M histogrammed values.
 4. The methodaccording to claim 3 and comprising clustering the M histogrammeddecimal values to determine M* clusters of decimal values that arecorrelated.
 5. The method according to claim 4 and comprisingdetermining a representative histogrammed decimal value for each of theM* clusters.
 6. The method according to claim 5 and comprisingdetermining a texture feature vector, TF, for the given ROI havingcomponents comprising the histogrammed frequencies of occurrence for theM* representative decimal values determined for the given ROI.
 7. Themethod according to claim 6 and comprising processing the texturefeature vector TF to determine whether the given ROI is malignant. 8.The method according to claim 7 wherein processing the texture featurevector TF comprises using a classifier to classify the TF.
 9. The methodaccording to claim 8 and comprising determining a contour feature vectorCF based on the determined contour and concatenating the texture featurevector TF and the contour feature vector CF to provide a feature vectorCF-TF.
 10. The method according to claim 9 wherein processing thefeature vector CF-TF comprises using a classifier to classify the CF-TF.11. The method according to claim 7 and comprising processing the X-rayimage to determine a contour for the given ROI.
 12. The method accordingto claim 11 and comprising processing the feature vector CF-TF todetermine whether the given CE-ROI is malignant.
 13. A method fordiagnosing breast cancer, the method comprising: receiving an image of agiven region of interest, ROI, in each X-ray image of a plurality ofX-ray images of a person's breast acquired at different times, the X-rayimage at each time comprising X-ray pixels that indicate intensity ofX-rays that passed through the breast to generate the image; determiningin accordance with claim 6 a texture feature vector TF for each image ofthe given ROI; processing the TFs to determine a time dependent featureof the given ROI; and using the time dependent feature to determinewhether the given CE-ROI is malignant.
 14. A non-transitorycomputer-readable medium comprising stored thereon executableinstructions that are executable to perform the method of claim
 13. 15.The method according to claim 1 wherein processing the M values todetermine whether the CE-ROI is malignant comprises using a classifier.16. The method according to claim 1 and comprising generating a BreastImaging Reporting and Data System (BIRADS) Lexicon Classification Systemcode for the given ROI to provide an indication as to whether the givenROI comprises a malignancy.
 17. The method according to claim 1 whereinthe X-ray image is a spectral contrast enhanced digital mammography(SCEDM) image.
 18. A non-transitory computer-readable medium comprisingstored thereon executable instructions that are executable to performthe method of claim 1.